






"""simple docstring"""


def        UpperCAmelCase      (					a__ = 60_08_51_47_51_43 ):





						'''simple docstring'''






						try:
												lowerCAmelCase   :List[str]						=			int(a__ )
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.' )
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.' )
						lowerCAmelCase   :List[Any]						=			2
						lowerCAmelCase   :Optional[Any]						=			0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowerCAmelCase   :Optional[Any]						=			i
												while n % i == 0:
																		lowerCAmelCase   :int						=			n // i
												i += 1
						return int(a__ )


if __name__ == "__main__":
		print(F"""{solution() = }""")







"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records